The role of landscape in shaping bird community and implications for landscape management at Nanjing Lukou International Airport

Abstract Understanding the patterns of bird diversity and its driving force is necessary for bird strike prevention. In this study, we investigated the effects of landscape on phylogenetic and functional diversity of bird communities at Nanjing Lukou International Airport (NLIA). Bird identifications and counting of individuals were carried out from November 2017 to October 2019. Based on the land‐cover data, the landscape was divided into four main types, including farmlands, woodlands, wetlands, and urban areas. Bird phylogenetic and functional diversity were strongly affected by landscape matrix types. Species richness and Faith's phylogenetic distance were highest in woodlands, while mean pairwise distance (MPD), mean nearest‐taxon distance (MNTD), and functional dispersion (FDis) were highest in wetlands. Based on the feeding behavior, carnivorous birds had the lowest species richness but had the highest FDis, which implied that carnivorous birds occupied most niches at the NLIA. Moreover, bird assemblages exhibited phylogenetic and functional clustering in the four kinds of landscapes. A variety of landscape attributes had significant effects on species diversity, phylogenetic and functional diversity. Landscape‐scale factors played an important role in the shaping of bird communities around NLIA. Our results suggest that landscape management surrounding airports can provide new approaches for policymakers to mitigate wildlife strikes.


| INTRODUC TI ON
Understanding species diversity patterns is helpful to determine strategies for conservation (Li et al., 2019). Birds are an important component of the ecosystem, and are considered indicator species for habitat quality because of their sensitivity to changes in habitat structure (Kirk et al., 2020;Moning & Müller, 2008). Previous studies have demonstrated that bird communities in agricultural and grassland landscapes have substantially declined due to habitat loss (Fischer et al., 2011;Pavlacky et al., 2022). Moreover, birds can respond to urbanization gradients worldwide by changing species richness, abundance, and composition (Filloy et al., 2019). Therefore, bird diversity is a valuable guide for conservation management at regional and landscape levels.
With human population growth, the majority of the land surface has been converted into a human-developed landscape (Pfeiffer et al., 2018). This continuous urban development leads to the fragmentation, isolation, and degradation of natural habitats, and is accompanied by severe impacts on the biotic communities living in urban landscapes (Schütz & Schulze, 2015). Many studies have shown that building cover is detrimental to bird richness and more generalist birds are found in human-developed habitats (Cresswell et al., 2020;Morelli, Benedetti, Floigl, & Ibáñez-Álamo, 2021;. One of the important urban landscapes is the airport which has irreplaceable functions in transportation (Pfeiffer et al., 2018).
However, bird strikes have been intensive with the development of the aviation industry, causing enormous economic losses worldwide each year (Jeffery & Buschke, 2019). Therefore, there is a specificity to the conservation of bird diversity in the airport area, that is, to seek a balance between reducing bird strikes and conserving biodiversity. Many studies focus on specific bird groups directly linked to the rate of bird strikes (Andersson et al., 2017;DeVault et al., 2016).
Compared with songbirds, waterfowls such as ducks and geese cause more damages to aircraft (Andersson et al., 2017), with their density, body mass, and group size significantly influencing the likelihood of aircraft damage (DeVault et al., 2016). Raptors also account for a high of bird strikes (Blackwell & Wright, 2006).
However, the diversity of bird communities in the areas surrounding airports has rarely been studied, although airports have been certified to preserve a high level of biodiversity . Several studies conducted in China indicate differences in bird diversity across landscapes, thus classified ecological environment management can be implemented to prevent bird strikes (Liu et al., 2021;Wu et al., 2015). Moreover, these studies merely focus on taxonomic diversity, without consideration of phylogenetic and functional diversity which have been studied in other landscapes (Frishkoff et al., 2014;Jia et al., 2020). Phylogenetic and functional diversity can provide information about bird phylogeny and functional traits, respectively, which help better characterize the community (Barbaro et al., 2014;Winter et al., 2013). It is widely recognized that phylogenetic and functional perspectives can be useful to disentangle the role of ecological processes (e.g., environmental filtering and competitive exclusion) which govern the assembly of bird communities (Gómez et al., 2010). Integrating phylogenetic and functional diversity is increasingly considered in wildlife management and conservation planning (Dehling et al., 2022;Winter et al., 2013). Evidence of biological invasions suggests the positive association of taxonomic, phylogenetic, and functional diversity with bird species richness, emphasizing the importance of considering different facets of biodiversity in wildlife management (Andrikou-Charitidou et al., 2020). A study conducted in protected areas in Spain indicates that taxonomic, functional, and phylogenetic diversity show differences among environment types, which suggests the importance of considering different facets of biodiversity simultaneously for a better spatial prioritization (Morelli, Benedetti, Floigl, & Ibáñez-Álamo, 2021;. These experiences can be used as a guide to study the pattern of bird diversity in different matrix types in and around the airport. Many studies concentrate on the primary variables underlying biodiversity near the airport in order to prevent bird strikes (Conkling et al., 2018). Biotic or abiotic factors, for instance, crop types, vegetation composition, food availability, and landscape structure, are proven to affect community composition at the airport (Alquezar et al., 2020;Iglay et al., 2017;Pennell et al., 2016).
Among the factors mentioned above, landscape structure is relatively less studied in the airport area. Most of the studies consider the effects of land use on biodiversity surrounding airports (Alquezar et al., 2020;Fox et al., 2013). Only a few studies indicate that the strike rate is positively influenced by large areas of wetlands, close proximity of wetlands, and landscape diversity at different extents from airports (Pfeiffer et al., 2018(Pfeiffer et al., , 2020. Fragmentation effects on some urban birds are linked to the type of peri-urban matrix (Hedblom & Söderström, 2010). Studies have shown that the proportion of developed land and forest edge density of cities have consistent negative effects on bird richness in urban landscapes (Filloy et al., 2019;Soifer et al., 2021). However, the effects of landscape attributes on bird diversity surrounding the airport remain poorly known. For the development of efficient and informed biodiversity policies to reduce the rate of bird strikes, it is essential to understand how landscape attributes affect biodiversity patterns on the landscape scale (Pfeiffer et al., 2020;Rüdisser et al., 2015).
In this study, we focused on the phylogenetic and functional diversity of bird communities and functional groups at Nanjing Lukou International Airport (NLIA), which is located in Jiangsu Province, China. We divided land-cover types into four main landscape matrix types (farmlands, woodlands, wetlands, and urban areas) and extracted landscape attributes of class level and landscape level to evaluate their effects on phylogenetic and functional richness and structure of bird assemblages. Here, we aim to address the following issues: (1) what is the pattern of bird diversity in different matrix types; (2) what is the pattern of bird functional groups' diversity; and (3) whether bird diversity and community structure are influenced by landscape attributes.

| Study area
We conducted the study within 8 km radius extent from the center of Nanjing Lukou International Airport (31°30′-31°56′N, 118°37′-119°60′E). The region experiences a subtropical monsoon climate with an annual mean temperature of 15.4°C and annual mean precipitation of 1106 mm. Land-cover data were obtained to evaluate the effects of landscape matrix types, which were downloaded from the Yangtze River Delta Science Data Center, National Earth System Science Data Center, National Science & Technology Infrastructure of China (http://nnu.geoda ta.cn:8008), with a resolution of 9 × 9 m per grid cell. We totally extracted 19 land-cover types through image interpretation in

| Bird survey
Wavelet analysis, a spectral decomposition technique, was used to determine the key spatial scale that was important to quantify the relative influence of matrix types on species distributions (Fortin et al., 2012). We generated 1000 random points within 8 km radius extent from the NLIA and extracted their land-cover types.
Then, we ran the wavelet analysis with MATLAB 9.9 (MathWorks, Natick, Massachusetts; https://ww2.mathw orks.cn/produ cts/ matlab.html). The result showed that a 288-m radius from the center of each point was appropriate ( Figure A1). We then randomly selected 9 sampling points in farmlands, 7 in wetlands, 5 in woodlands, and 4 in urban areas (25 points in total) to make sure that sampling effort on each matrix type was roughly proportional to its area ( Figure 1). Sampling points were at least 576 m apart.
From November 2017 to October 2019, we carried out point-count bird surveys monthly (Sorace et al., 2000). Each observation was in 20 min. We usually surveyed birds on sunny and windless days. We recorded all birds seen or heard and excluded flyovers to obtain the birds' data.
Considering that the combination of functional groups and landscape matrices enables a comprehensive assessment of bird diversity (French & Picozzi, 2002), we chose three main functional groups (i.e., carnivorous birds, insectivorous birds, and omnivorous birds) based on the traits data collected by Wang et al., 2021 (Table A1).
The species accumulation curve (SAC) is a useful tool to determine the validity and adequacy of sampling (Ugland et al., 2003). We used R package "vegan" (Oksanen et al., 2013) to plot species' accumulation curve for bird communities in each matrix type.

| Bird phylogeny and functional traits
We cut the global phylogenetic tree of birds by subsampling 5000 "Hackett All species: a set of 10,000 trees with 9993 OTUs each" trees from BirdTree (http://birdt ree.org; Jetz et al., 2012). Then, we constructed a new maximum clade credibility tree with a 0.5 posterior probability limit by the software TreeAnnonator v1.10.4 in the BEAST package v1.10.4 (Suchard et al., 2018).
We focused on four kinds of functional traits relevant ecologically to bird strikes: morphological characteristics (body length, bill length, wing length, tarsus length, and body mass), nest sites (ground, water, shrub, canopy, and wall), feeding behaviors (carnivorous, insectivorous, omnivorous, granivorous, and piscivorous), and F I G U R E 1 The location of the sampling points and landscape types at Nanjing Lukou International Airport (NLIA).

| Phylogenetic and functional diversity measures
Based on the new phylogenetic tree constructed, we calculated Faith's phylogenetic distance (Faith's PD) to describe the total sum of phylogenetic history (Faith, 1992). We also calculated mean pairwise distance (MPD) and mean nearest-taxon distance (MNTD) to represent phylogenetic structure (Webb et al., 2002). We used null models to infer whether communities exhibited phylogenetic clustering or over-dispersion (Jia et al., 2020). We calculated the standardized effect size (SES) for values of MPD and MNTD based on 999 null models using R package "picante" (Kembel et al., 2010).
We computed functional richness (FRic) and functional dispersion (FDis) to represent functional diversity and structure. Then, we infer whether communities exhibited functional clustering or over-dispersion in the same way as for phylogenetic diversity. With the species-by-trait matrix, we first constructed a functional dendrogram using Gower distance with UPGMA method (Petchey & Gaston, 2002). We computed mean pairwise functional distance (MFD) and mean functional nearest-taxon distance (FD.MNTD) based on the functional dendrogram. Then, we calculated SES for MFD and FD.MNTD. The value of SES indicates phylogenetic or functional clustering when <0, while over-dispersion generates values higher than 0 (Si et al., 2017). All metrics were calculated using R package "picante" (Kembel et al., 2010) and "FD" (Laliberté & Legendre, 2010). Species richness (SR), MPD, MNTD, and FDis of functional groups were also calculated to evaluate phylogenetic and functional structure of different functional groups.

| Statistical analysis
Considering the non-normal distribution of the data, we performed the multiple-comparison test after the Kruskal-Wallis test to search for differences in SR, PD, MPD, MNTD, FRic, and FDis for the entire community (Giraudoux et al., 2018). We used the one-sample t test to determine whether SES was significantly different from 0. The level of statistical significance was set to 0.05. To evaluate the effects of landscape attributes on bird diversity, we first removed outliers from original data and executed missing values imputation with random forest regression (Xia et al., 2017;Zuur et al., 2010). We then used Z-score transformation to standardize the original data. We calculated Pearson correlation coefficient (r) to check the pairwise correlations between the predictor variables (Dormann et al., 2013). Then, we constructed the dendrogram for landscape metrics based on the distance (1 -Pearson's r), thereby selecting the metrics with the value of |r| to be <0.70 (i.e., selecting one metric in a clade; Figures A2, A3). Then, we built a set of multiple linear regression models by combining the variables retained and used the corrected Akaike information criterion (AICc)

F I G U R E 3
to rank models. We performed normality tests on the regression residuals to make sure these multiple linear regression models were robust. Variables selection was based on the models with ΔAICc < 2.
We then reconstructed candidate models with variables selected.
Given that Akaike weight (w i ) indicated that no model was obviously the best (w i > 0.9; Anderson et al., 2001), we used the model average method to calculate the relative importance (w + ), averaged parameter estimates, and standard errors of variables selected. Model average was carried out with R package "MuMIn" (Bartoń, 2020). All statistical analyses were performed in R 4.0.1 (R Core Team, 2020).

| RE SULTS
After 2 years of investigation, we acquired 453 samples that could be used for subsequent analysis. Species accumulation curves rose smoothly, indicating that we sampled adequately for the four matrix types (Figure 2). From November 2017 to October 2019, 149 bird species were detected at Nanjing Lukou International Airport ( χ 2 = 36.484, p < 0.001). MPD was highest in wetlands, and MPD was significantly higher in farmlands than in urban areas and woodlands (χ 2 = 84.617, p < 0.001). MNTD was highest in wetlands, while MNTD was significantly higher in farmlands than in woodlands (χ 2 = 61.747, p < 0.001). No significant difference was found in MNTD between farmlands and urban areas and between urban areas and woodlands.
FRic was significantly lower in urban areas than in other matrix types (χ 2 = 19.156, p < 0.001). FDis was highest in wetlands, while FDis was significantly higher in farmlands than in urban areas (χ 2 = 56.328, p < 0.001). No significant difference was found in FDis between farmlands and woodlands and between woodlands and urban areas ( Figure 3).
Species richness of omnivorous birds was higher than insectivorous and carnivorous birds in all matrix types. However, changes in phylogenetic and functional structure were asynchronous. FDis of carnivorous birds was significantly higher than omnivorous and carnivorous birds while there were no significant differences between MPD and MNTD of functional groups (Figure 4).

| DISCUSS ION
A better understanding of bird community and its drivers is necessary for sustainable urban planning and bird conservation in the areas surrounding airports (Aronson et al., 2014;Hu et al., 2020). It has been proved that bird community composition is directly linked to the probability of bird strikes , emphasizing the need of studying bird community in areas surrounding airports.
In this study, we find that landscape matrix types play an important role in shaping bird communities. At Nanjing Lukou International Airport, species richness is highest in woodlands, indicating that woodlands near airports may support more bird species (Figure 3).
Woodlands represent hotspots of urban biodiversity, and bird species richness is supported by tree cover (Ferenc et al., 2014). Green Based on niche theory, the establishment of bird species might be facilitated by the availability of niches (Sayol et al., 2021). High levels of functional richness illustrate that more functional niches are occupied in woodlands, wetlands, and farmlands. Although more genetic clades can be detected in woodlands, species are less closely related compared to bird assemblages in wetlands, indicating that woodlands contain more evolutionarily unique species (Mestre et al., 2020). The highest FDis of bird assemblages shows that resources in wetlands are sufficiently used by functionally distinct species. In farmlands and urban areas, low phylogenetic and functional diversity may be related to human disturbance and urbanization (Beninde et al., 2015).
Identification and analysis of ecological groups have been fundamental to understanding the entire community structure (Sohil & Sharma, 2020). In this study, we found that the species richness of omnivores is the highest, then insectivores and carnivores at the NLIA.
However, changes in phylogenetic and functional structure of functional groups are asynchronous (Figure 4). We can infer that carnivorous F I G U R E 5 Standardized effect sizes (SES) of phylogenetic and functional diversity and their 95% confidence intervals (with p-values of one-sample t tests) of bird communities in different matrix types at Nanjing Lukou International Airport (NLIA). SES. MPD is the standardized effect size of mean pairwise distance. SES.MNTD is the standardized effect size of mean nearest-taxon distance. SES.MFD is the standardized effect size of mean pairwise functional distance. SES.FD.MNTD is the standardized effect size of mean functional nearest-taxon distance. The gray dash line indicates that the SES value is equal to 0, which is the dividing line to determine whether the community structure is clustered or over-dispersed.
birds are more related in lineages than insectivorous and omnivorous birds. Functional traits related to feeding behaviors and foraging strata can reflect the situation of food resources occupation and trophic niche (Si et al., 2017). The analysis of functional structure reveals that carnivores distribute more uniformly in the functional space than insectivores and carnivores (Figure 4d), which indicates that carnivorous birds may occupy more food resources and niches at the NLIA.
The entire community exhibits phylogenetic and functional clustering in all landscape matrix types ( Figure 5), which implies environmental filtering governs community assembly at the NLIA (García-Navas & Thuiller, 2020). The process of bird community assembly in small scales may be dominated by competitive exclusion because limited resources are available (Gómez et al., 2010). A study has found that environmental filtering results in marked shifts in the composition of communities because local and regional habitat structure and function may be changed by urbanization, excluding non-synanthropic species (Evans et al., 2018). It has been proved that urbanization acts as an environmental filter that governs the assembly of bird community (Schütz & Schulze, 2015). We think that the situation at the NLIA may be the same. In human-dominated landscapes, local habitat structure is supposed to change due to human disturbance. The abiotic factors function as environmental filters where particular species are selected as a result of their capacity to survive and persist in a given space, resulting in clustered patterns of phylogenetic and functional structure  *p < 0.05; **p < 0.01; ***p < 0.001. (Adorno et al., 2021;Kraft et al., 2015). We also find that woodlands have the strongest filtering effects, which reinforces the role of woodlands in shaping bird communities.
Patchy and regular farmlands have negative effects on phylogenetic and functional richness of bird communities ( Table 2).
Evolutionary history is lost because evolutionarily distinct species are more likely to be extinct in farmlands (Frishkoff et al., 2014). We find that the number of phylogenetically related species increases in patchy and fragmented urban areas, indicating the loss of evolutionarily unique branches (Table 2) woodlands. We also find that functional richness of bird communities decreases in fragmented woodlands and the functional structure turns to be simplified with the decrease in areas of woodlands (Table 2). This phenomenon can be interpreted by the shape complexity of woodlands and urban areas. Complex shape may enrich edge structure that has a major impact on birds' selection of vegetation and food resources (Melin et al., 2018). The landscape attributes of wetlands have little effect on bird communities. Only the number of patches of wetlands influences the phylogenetic distance and numbers of phylogenetically related species.
At landscape level, the mean of patch area has the strongest explanatory effects. Species richness and phylogenetic richness respond positively to the mean of patch area ( Table 3), indicating that more distantly related birds can be observed with the increase in the mean of patch area. Many studies have proven that landscape diversity reflects landscape heterogeneity level and bird diversity responds positively to landscape heterogeneity (Katayama et al., 2014;Redlich et al., 2018).
We find landscape diversity affects bird functional structure positively at the NLIA (Table 3). High landscape heterogeneity near airports means more niches and natural resources are available (Pfeiffer et al., 2018), thus making birds' functional structure complicated.
In this study, 149 bird species were detected at Nanjing Lukou International Airport. Based on the land-cover data, the landscape was divided into four main types, including farmlands, woodlands, wetlands, and urban areas. Bird species richness, and phylogenetic and functional diversity were strongly affected by landscape matrix types. Species richness and Faith's phylogenetic distance were highest in woodlands, while mean pairwise distance (MPD), mean nearest-taxon distance (MNTD), and functional dispersion (FDis) were highest in wetlands. Based on the feeding behavior, carnivorous birds had the lowest species richness, but had the highest FDis, which implied that carnivorous birds occupied most niches at the NLIA. This study suggests that functional groups affect the structure of bird communities in different matrix types. We also find that the landscape surrounding the NLIA acts as an environmental filter that governs the bird community assembly, and landscape attributes of different matrix types affect bird diversity. Our results suggest that landscape management surrounding airports can provide new approaches for policymakers to mitigate wildlife strikes.  funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

CO N FLI C T O F I NTE R E S T
The authors declare that they have no conflict of interest. Abbreviations: LC, least-concern species; NT, near-threatened species; and VU, vulnerable species.

TA B L E A 1 (Continued)
TA B L E A 2 Traits used to measure functional diversity and phylogenetic signals of traits.  Note: Both probabilities resulting from Brownian (p Brownian ) and random (p random ) phylogenetic structures were exhibited.

F I G U R E A 2
The clustering of landscape metrics at class level for Pearson's correlation. The dendrogram was constructed based on the distance (1-Pearson's r).

F I G U R E A 3
The clustering of landscape metrics at landscape level for Pearson's correlation. The dendrogram was constructed based on the distance (1-Pearson's r).